Abstract
Neural machine translation (NMT) is a state-of-the-art technique in the task of machine translation (MT), where a source-language text is converted into a target language text while preserving its meaning. NMT attracts attention because it handles sequence to sequence learning problems for variable-length source and target sentences. With the attention mechanism, the NMT system performs well in the context-analyzing ability. But it needs sufficient parallel training corpus, which is a challenge in low resource language scenario. To overcome the bar of a handy parallel corpus, there is an increase in demand for direct translation among similar language pairs. This paper investigates the NMT system for direct translation of low resource similar language pair: Assamese–Bengali. The main contribution of this work is Assamese–Bengali parallel corpus. The NMT system has achieved a bilingual evaluation understudy (BLEU) score of 7.20 for Assamese to Bengali translation and BLEU score 10.10 for Bengali to Assamese translation, respectively.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
References
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015). http://arxiv.org/abs/1409.0473
Cho, K., van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder–decoder approaches. In: Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation, pp. 103–111. Association for Computational Linguistics, Doha, Qatar (2014). https://doi.org/10.3115/v1/W14-4012
Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder–decoder for statistical machine translation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1724–1734. Association for Computational Linguistics, Doha, Qatar (2014). https://doi.org/10.3115/v1/D14-1179
Ghosh, S., Girish, K.V.V., Sreenivas, T.: Relationship between indian languages using long distance bi-gram language models. In: Proceedings of ICON 2011, 9th International Conference on Natural Language Processing, pp. 104–113. Macmillan (2011)
Josan, G., Lehal, G.: A Punjabi to Hindi machine translation system, pp. 157–160 (2008)
Klein, G., Kim, Y., Deng, Y., Senellart, J., Rush, A.: OpenNMT: Open-source toolkit for neural machine translation. In: Proceedings of ACL 2017, System Demonstrations, pp. 67–72. Association for Computational Linguistics, Vancouver, Canada (2017). https://www.aclweb.org/anthology/P17-4012
Koehn, P.: Statistical Machine Translation. Cambridge University Press (2009). https://doi.org/10.1017/CBO9780511815829
Koehn, P., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., Bertoldi, N., Cowan, B., Shen, W., Moran, C., Zens, R., Dyer, C., Bojar, O., Constantin, A., Herbst, E.: Moses: Open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions, pp. 177–180. Association for Computational Linguistics, Prague, Czech Republic (2007). https://www.aclweb.org/anthology/P07-2045
Laskar, S.R., Dutta, A., Pakray, P., Bandyopadhyay, S.: Neural machine translation: English to Hindi. In: 2019 IEEE Conference on Information and Communication Technology, pp. 1–6 (2019)
Laskar, S.R., Pakray, P., Bandyopadhyay, S.: Neural machine translation: Hindi-Nepali. In: Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pp. 202–207. Association for Computational Linguistics, Florence, Italy (2019). https://doi.org/10.18653/v1/W19-5427
Luong, T., Pham, H., Manning, C.D.: Effective approaches to attention-based neural machine translation. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1412–1421. Association for Computational Linguistics, Lisbon, Portugal (2015). https://doi.org/10.18653/v1/D15-1166
Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: A method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL ’02, pp. 311–318. Association for Computational Linguistics, Stroudsburg, PA, USA (2002). https://doi.org/10.3115/1073083.1073135
Pathak, A., Pakray, P.: Neural machine translation for Indian languages. J. Intell. Syst. pp. 1–13 (2018). https://doi.org/10.1515/jisys-2018-0065
Pathak, A., Pakray, P., Bentham, J.: English–Mizo machine translation using neural and statistical approaches. Neur. Comput. Appl. 30, 1–17 (2018). https://doi.org/10.1007/s00521-018-3601-3
Saini, S., Sahula, V.: Neural machine translation for English to Hindi, pp. 1–6 (2018). https://doi.org/10.1109/INFRKM.2018.8464781
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 27, pp. 3104–3112. Curran Associates, Inc. (2014). http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf
Acknowledgements
We would like to thank Department of Computer Science and Engineering and Center for Natural Language Processing (CNLP) at National Institute of Technology Silchar for providing the requisite support and infrastructure to execute this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Laskar, S.R., Pakray, P., Bandyopadhyay, S. (2021). Neural Machine Translation: Assamese–Bengali. In: Das, B., Patgiri, R., Bandyopadhyay, S., Balas, V.E. (eds) Modeling, Simulation and Optimization. Smart Innovation, Systems and Technologies, vol 206. Springer, Singapore. https://doi.org/10.1007/978-981-15-9829-6_45
Download citation
DOI: https://doi.org/10.1007/978-981-15-9829-6_45
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-9828-9
Online ISBN: 978-981-15-9829-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)